Large-scale composite hypothesis testing procedure for omics data analyses.

ABSTRACT: Composite hypothesis testing using summary statistics is a well-established approach for assessing the effect of a single marker or gene across multiple traits or omics levels. Numerous procedures have been developed for this task and have been successfully applied to identify complex patterns of association between traits, conditions, or phenotypes. However, existing methods often struggle with scalability in large datasets or fail to account for dependencies between traits or omics levels, limiting their ability to control false positives effectively. To overcome these challenges, we present the qch_copula approach, which integrates mixture models with a copula function to capture dependencies between traits or omics and provides rigorously defined P-values for any composite hypot

SUBMITTER: De Walsche A 

PROVIDER: S-EPMC12412788 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature